数据分析与挖掘

接着案例一，我们再使用另一种方法实例一个案例

直接上代码：

#!/usr/bin/Rscript

library(plyr)
library(reshape2)

#1、根据训练集创建朴素贝叶斯分类器
#1.1、生成类别的概率

##计算训练集合D中类别出现的概率，即P{c_i}
##输入：trainData 训练集，类型为数据框
##      strClassName 指明训练集中名称为    strClassName列为分类结果
##输出：数据框，P{c_i}的集合，类别名称|概率（列名为 prob）
class_prob <- function(trainData, strClassName){
    #训练集样本数
    #nrow返回行数
    length.train <- nrow(trainData)
    dTemp <- ddply(trainData, strClassName, "nrow")
    dTemp <- ddply(dTemp, strClassName, mutate, prob = nrow/length.train)
    dTemp[,-2]
}

##1.2、生成每个类别下，特征取不同值的概率
##计算训练集合D中,生成每个类别下，特征取不同值的概率，即P{fi|c_i}
##输入：trainData 训练集，类型为数据框
##      strClassName 指明训练集中名称为strClassName列为分类结果，其余的全部列认为是特征值
##输出：数据框，P{fi|c_i}的集合，类别名称|特征名称|特征取值|概率（列名为 prob）
feature_class_prob <- function(trainData, strClassName){
    # 横表转换为纵表
    data.melt <- melt(trainData,id=c(strClassName))
    # 统计频数
    aa <- ddply(data.melt, c(strClassName,"variable","value"), "nrow")
    # 计算概率
    bb <- ddply(aa, c(strClassName,"variable"), mutate, sum = sum(nrow), prob = nrow/sum)
    # 增加列名
    colnames(bb) <- c("class.name",
                    "feature.name",
                    "feature.value",
                    "feature.nrow",
                    "feature.sum",
                    "prob")
    # 返回结果
    bb[,c(1,2,3,6)]
}

## 以上创建完朴素贝叶斯分类器


## 2、使用生成的朴素贝叶斯分类器进行预测
##使用生成的朴素贝叶斯分类器进行预测P{fi|c_i}
##输入：oneObs 数据框，待预测的样本，格式为 特征名称|特征值
##      pc 数据框，训练集合D中类别出现的概率，即P{c_i}  类别名称|概率
##      pfc 数据框，每个类别下，特征取不同值的概率，即P{fi|c_i}
##                  类别名称|特征名称|特征值|概率
##输出：数据框，待预测样本的分类对每个类别的概率，类别名称|后验概率（列名为 prob）
pre_class <- function(oneObs, pc,pfc){
    colnames(oneObs) <- c("feature.name", "feature.value")
    colnames(pc) <- c("class.name","prob")
    colnames(pfc) <- c("class.name","feature.name","feature.value","prob")

    # 取出特征的取值的条件概率
    feature.all <- join(oneObs,pfc,by=c("feature.name","feature.value"),type="inner")
    # 取出特征取值的条件概率连乘
    feature.prob <- ddply(feature.all,.(class.name),summarize,prob_fea=prod(prob))  #prod为连乘函数

    #取出类别的概率
    class.all <- join(feature.prob,pc,by="class.name",type="inner")
    #输出结果
    ddply(class.all,.(class.name),mutate,pre_prob=prob_fea*prob)[,c(1,4)]
}


##3、数据测试
##用上面苹果的数据作为例子进行测试
#训练集
train.apple <-data.frame(
    size=c("大","小","大","大","小","小"),
    weight=c("轻","重","轻","轻","重","轻"),
    color=c("红","红","红","绿","红","绿"),
    taste=c("good","good","bad","bad","bad","good")
)
#待预测样本
oneObs<-data.frame(
    feature.name =c("size", "weight", "color"),
    feature.value =c("大","重","红")
)

#预测分类
pc <- class_prob(train.apple,"taste")
pfc <- feature_class_prob(train.apple,"taste")
pre_class(oneObs, pc, pfc)

预测结果为：

class.name pre_prob
1 bad 0.07407407
2 good 0.03703704

可见该苹果的口味为：bad

*********************************************这里是分割线****************************************************

我们使用这个方法再预测一下案例一中的数据集。

#数据集样本
data <- data.frame(c("sunny","hot","high","weak","no",  
                 "sunny","hot","high","strong","no",  
                 "overcast","hot","high","weak","yes",  
                 "rain","mild","high","weak","yes",  
                 "rain","cool","normal","weak","yes",  
                 "rain","cool","normal","strong","no",  
                 "overcast","cool","normal","strong","yes",  
                 "sunny","mild","high","weak","no",  
                 "sunny","cool","normal","weak","yes",  
                 "rain","mild","normal","weak","yes",  
                 "sunny","mild","normal","strong","yes",  
                 "overcast","mild","high","strong","yes",  
                 "overcast","hot","normal","weak","yes",  
                 "rain","mild","high","strong","no"), 
                 byrow = TRUE,
                 dimnames = list(day = c(),condition = c("outlook","temperature","humidity","wind","playtennis")), 
                 nrow=14, 
                 ncol=5);  

#待预测样本
ddata<-data.frame(
    feature.name =c("outlook", "temperature","humidity","wind"),
    feature.value =c("overcast","mild","normal","weak")
)


#预测分类
pc <- class_prob(data,"playtennis")
pfc <- feature_class_prob(data,"playtennis")
pre_class(ddata, pc, pfc)

预测结果为：

class.name   pre_prob
1         no 0.02666667
2        yes 0.13168724

预测结果为：yes，可见与案例一的结果一样。